Methods for addressing class imbalance can be broadly grouped into three categories: data-level techniques, algorithm-level methods, and hybrid approaches [1]}. Data-level techniques aim to reduce the level of class imbalance through some form of sampling, for example oversampling the minority or undersampling the majority class. However, oversampling can lead to overfitting, and undersampling to a loss of important information [2]}. Algorithm-level methods, commonly implemented with a weight or cost scheme, involve modifying the underlying learner or its output in order to reduce bias toward the majority class. In cost-sensitive learning, penalties are assigned to each class through a cost matrix, though its definition often needs to be done empirically, or using expert domain knowledge [1]}. Hybrid approaches combine data-level and algorithm-level approaches in various ways to handle the class imbalance problem. Examples include RUSBoost [4]} and the recently-proposed HD-Ensemble method of [5]}.
